Overview

Dataset statistics

Number of variables33
Number of observations5963
Missing cells130551
Missing cells (%)66.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory264.0 B

Variable types

Numeric20
Categorical13

Alerts

Location Name has a high cardinality: 3753 distinct values High cardinality
Year is highly correlated with Tsu and 4 other fieldsHigh correlation
Tsu is highly correlated with YearHigh correlation
Vol is highly correlated with Injuries and 1 other fieldsHigh correlation
MMI Int is highly correlated with Deaths and 2 other fieldsHigh correlation
Deaths is highly correlated with Year and 19 other fieldsHigh correlation
Death Description is highly correlated with Year and 14 other fieldsHigh correlation
Missing is highly correlated with Deaths and 13 other fieldsHigh correlation
Missing Description is highly correlated with Deaths and 12 other fieldsHigh correlation
Injuries is highly correlated with Vol and 18 other fieldsHigh correlation
Injuries Description is highly correlated with Deaths and 16 other fieldsHigh correlation
Damage ($Mil) is highly correlated with Injuries and 7 other fieldsHigh correlation
Damage Description is highly correlated with Deaths and 16 other fieldsHigh correlation
Houses Destroyed is highly correlated with Deaths and 17 other fieldsHigh correlation
Houses Destroyed Description is highly correlated with Deaths and 14 other fieldsHigh correlation
Houses Damaged is highly correlated with Deaths and 19 other fieldsHigh correlation
Houses Damaged Description is highly correlated with Damage Description and 8 other fieldsHigh correlation
Total Deaths is highly correlated with Year and 20 other fieldsHigh correlation
Total Death Description is highly correlated with Year and 15 other fieldsHigh correlation
Total Missing is highly correlated with Deaths and 11 other fieldsHigh correlation
Total Missing Description is highly correlated with Missing and 7 other fieldsHigh correlation
Total Injuries is highly correlated with Vol and 20 other fieldsHigh correlation
Total Injuries Description is highly correlated with Deaths and 18 other fieldsHigh correlation
Total Damage ($Mil) is highly correlated with Injuries and 6 other fieldsHigh correlation
Total Damage Description is highly correlated with Deaths and 18 other fieldsHigh correlation
Total Houses Destroyed is highly correlated with Deaths and 21 other fieldsHigh correlation
Total Houses Destroyed Description is highly correlated with Deaths and 14 other fieldsHigh correlation
Total Houses Damaged is highly correlated with MMI Int and 22 other fieldsHigh correlation
Total Houses Damaged Description is highly correlated with Damage Description and 8 other fieldsHigh correlation
Tsu is highly correlated with VolHigh correlation
Vol is highly correlated with TsuHigh correlation
Focal Depth (km) is highly correlated with MissingHigh correlation
Deaths is highly correlated with Missing and 4 other fieldsHigh correlation
Death Description is highly correlated with Missing Description and 4 other fieldsHigh correlation
Missing is highly correlated with Focal Depth (km) and 7 other fieldsHigh correlation
Missing Description is highly correlated with Death Description and 6 other fieldsHigh correlation
Injuries is highly correlated with Deaths and 9 other fieldsHigh correlation
Injuries Description is highly correlated with Death Description and 6 other fieldsHigh correlation
Damage ($Mil) is highly correlated with Houses Destroyed and 4 other fieldsHigh correlation
Damage Description is highly correlated with Injuries Description and 7 other fieldsHigh correlation
Houses Destroyed is highly correlated with Injuries and 5 other fieldsHigh correlation
Houses Destroyed Description is highly correlated with Injuries Description and 6 other fieldsHigh correlation
Houses Damaged is highly correlated with Injuries and 5 other fieldsHigh correlation
Houses Damaged Description is highly correlated with Damage Description and 4 other fieldsHigh correlation
Total Deaths is highly correlated with Deaths and 4 other fieldsHigh correlation
Total Death Description is highly correlated with Death Description and 6 other fieldsHigh correlation
Total Missing is highly correlated with Deaths and 6 other fieldsHigh correlation
Total Missing Description is highly correlated with Missing Description and 3 other fieldsHigh correlation
Total Injuries is highly correlated with Deaths and 10 other fieldsHigh correlation
Total Injuries Description is highly correlated with Death Description and 7 other fieldsHigh correlation
Total Damage ($Mil) is highly correlated with Damage ($Mil)High correlation
Total Damage Description is highly correlated with Death Description and 8 other fieldsHigh correlation
Total Houses Destroyed is highly correlated with Missing and 7 other fieldsHigh correlation
Total Houses Destroyed Description is highly correlated with Injuries Description and 6 other fieldsHigh correlation
Total Houses Damaged is highly correlated with Injuries and 5 other fieldsHigh correlation
Total Houses Damaged Description is highly correlated with Damage Description and 4 other fieldsHigh correlation
Year is highly correlated with TsuHigh correlation
Tsu is highly correlated with YearHigh correlation
Vol is highly correlated with InjuriesHigh correlation
Deaths is highly correlated with Death Description and 8 other fieldsHigh correlation
Death Description is highly correlated with Deaths and 8 other fieldsHigh correlation
Missing is highly correlated with Deaths and 6 other fieldsHigh correlation
Missing Description is highly correlated with Deaths and 11 other fieldsHigh correlation
Injuries is highly correlated with Vol and 9 other fieldsHigh correlation
Injuries Description is highly correlated with Deaths and 9 other fieldsHigh correlation
Damage ($Mil) is highly correlated with Damage Description and 2 other fieldsHigh correlation
Damage Description is highly correlated with Damage ($Mil) and 10 other fieldsHigh correlation
Houses Destroyed is highly correlated with Injuries and 12 other fieldsHigh correlation
Houses Destroyed Description is highly correlated with Damage Description and 8 other fieldsHigh correlation
Houses Damaged is highly correlated with Missing Description and 9 other fieldsHigh correlation
Houses Damaged Description is highly correlated with Damage Description and 8 other fieldsHigh correlation
Total Deaths is highly correlated with Deaths and 10 other fieldsHigh correlation
Total Death Description is highly correlated with Deaths and 10 other fieldsHigh correlation
Total Missing is highly correlated with Missing and 4 other fieldsHigh correlation
Total Missing Description is highly correlated with Missing and 5 other fieldsHigh correlation
Total Injuries is highly correlated with Deaths and 8 other fieldsHigh correlation
Total Injuries Description is highly correlated with Deaths and 10 other fieldsHigh correlation
Total Damage ($Mil) is highly correlated with Damage ($Mil) and 2 other fieldsHigh correlation
Total Damage Description is highly correlated with Damage ($Mil) and 10 other fieldsHigh correlation
Total Houses Destroyed is highly correlated with Missing Description and 13 other fieldsHigh correlation
Total Houses Destroyed Description is highly correlated with Damage Description and 8 other fieldsHigh correlation
Total Houses Damaged is highly correlated with Missing Description and 9 other fieldsHigh correlation
Total Houses Damaged Description is highly correlated with Damage Description and 8 other fieldsHigh correlation
Missing Description is highly correlated with Total Missing DescriptionHigh correlation
Total Death Description is highly correlated with Death DescriptionHigh correlation
Houses Destroyed Description is highly correlated with Total Houses Destroyed Description and 3 other fieldsHigh correlation
Total Houses Damaged Description is highly correlated with Houses Damaged DescriptionHigh correlation
Total Houses Destroyed Description is highly correlated with Houses Destroyed Description and 3 other fieldsHigh correlation
Total Missing Description is highly correlated with Missing DescriptionHigh correlation
Houses Damaged Description is highly correlated with Houses Destroyed Description and 2 other fieldsHigh correlation
Damage Description is highly correlated with Houses Destroyed Description and 2 other fieldsHigh correlation
Injuries Description is highly correlated with Total Injuries DescriptionHigh correlation
Death Description is highly correlated with Total Death DescriptionHigh correlation
Total Damage Description is highly correlated with Houses Destroyed Description and 2 other fieldsHigh correlation
Total Injuries Description is highly correlated with Injuries DescriptionHigh correlation
Tsu is highly correlated with Longitude and 5 other fieldsHigh correlation
Vol is highly correlated with Latitude and 8 other fieldsHigh correlation
Latitude is highly correlated with Vol and 1 other fieldsHigh correlation
Longitude is highly correlated with Tsu and 2 other fieldsHigh correlation
MMI Int is highly correlated with VolHigh correlation
Deaths is highly correlated with Missing and 10 other fieldsHigh correlation
Death Description is highly correlated with Vol and 4 other fieldsHigh correlation
Missing is highly correlated with Tsu and 6 other fieldsHigh correlation
Missing Description is highly correlated with Tsu and 8 other fieldsHigh correlation
Injuries is highly correlated with Deaths and 8 other fieldsHigh correlation
Injuries Description is highly correlated with Vol and 8 other fieldsHigh correlation
Damage ($Mil) is highly correlated with Tsu and 7 other fieldsHigh correlation
Damage Description is highly correlated with Injuries Description and 8 other fieldsHigh correlation
Houses Destroyed is highly correlated with Deaths and 8 other fieldsHigh correlation
Houses Destroyed Description is highly correlated with Death Description and 7 other fieldsHigh correlation
Houses Damaged is highly correlated with Deaths and 8 other fieldsHigh correlation
Houses Damaged Description is highly correlated with Vol and 7 other fieldsHigh correlation
Total Deaths is highly correlated with Deaths and 10 other fieldsHigh correlation
Total Death Description is highly correlated with Death Description and 7 other fieldsHigh correlation
Total Missing is highly correlated with Tsu and 6 other fieldsHigh correlation
Total Missing Description is highly correlated with Deaths and 8 other fieldsHigh correlation
Total Injuries is highly correlated with Deaths and 8 other fieldsHigh correlation
Total Injuries Description is highly correlated with Vol and 8 other fieldsHigh correlation
Total Damage ($Mil) is highly correlated with Tsu and 7 other fieldsHigh correlation
Total Damage Description is highly correlated with Injuries Description and 7 other fieldsHigh correlation
Total Houses Destroyed is highly correlated with Deaths and 12 other fieldsHigh correlation
Total Houses Destroyed Description is highly correlated with Vol and 8 other fieldsHigh correlation
Total Houses Damaged is highly correlated with Deaths and 8 other fieldsHigh correlation
Total Houses Damaged Description is highly correlated with Vol and 7 other fieldsHigh correlation
Tsu has 4154 (69.7%) missing values Missing
Vol has 5892 (98.8%) missing values Missing
Focal Depth (km) has 2801 (47.0%) missing values Missing
Mag has 1679 (28.2%) missing values Missing
MMI Int has 3157 (52.9%) missing values Missing
Deaths has 3962 (66.4%) missing values Missing
Death Description has 3511 (58.9%) missing values Missing
Missing has 5939 (99.6%) missing values Missing
Missing Description has 5939 (99.6%) missing values Missing
Injuries has 4719 (79.1%) missing values Missing
Injuries Description has 4518 (75.8%) missing values Missing
Damage ($Mil) has 5457 (91.5%) missing values Missing
Damage Description has 1654 (27.7%) missing values Missing
Houses Destroyed has 5176 (86.8%) missing values Missing
Houses Destroyed Description has 4259 (71.4%) missing values Missing
Houses Damaged has 5469 (91.7%) missing values Missing
Houses Damaged Description has 4992 (83.7%) missing values Missing
Total Deaths has 4184 (70.2%) missing values Missing
Total Death Description has 3856 (64.7%) missing values Missing
Total Missing has 5936 (99.5%) missing values Missing
Total Missing Description has 5933 (99.5%) missing values Missing
Total Injuries has 4700 (78.8%) missing values Missing
Total Injuries Description has 4498 (75.4%) missing values Missing
Total Damage ($Mil) has 5485 (92.0%) missing values Missing
Total Damage Description has 2632 (44.1%) missing values Missing
Total Houses Destroyed has 5146 (86.3%) missing values Missing
Total Houses Destroyed Description has 4183 (70.1%) missing values Missing
Total Houses Damaged has 5522 (92.6%) missing values Missing
Total Houses Damaged Description has 5099 (85.5%) missing values Missing
Deaths is highly skewed (γ1 = 21.11729164) Skewed
Injuries is highly skewed (γ1 = 24.75840512) Skewed
Houses Destroyed is highly skewed (γ1 = 25.40501293) Skewed
Houses Damaged is highly skewed (γ1 = 20.24621769) Skewed
Total Injuries is highly skewed (γ1 = 22.55838787) Skewed
Total Houses Destroyed is highly skewed (γ1 = 25.26872884) Skewed
Total Houses Damaged is highly skewed (γ1 = 20.95380115) Skewed

Reproduction

Analysis started2022-01-08 22:21:17.117661
Analysis finished2022-01-08 22:23:07.786015
Duration1 minute and 50.67 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Year
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct922
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1809.625021
Minimum-2150
Maximum2021
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)0.7%
Memory size46.7 KiB
2022-01-08T23:23:08.267727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2150
5-th percentile1067.1
Q11823
median1929
Q31990
95-th percentile2016
Maximum2021
Range4171
Interquartile range (IQR)167

Descriptive statistics

Standard deviation369.1792014
Coefficient of variation (CV)0.2040086742
Kurtosis20.38200133
Mean1809.625021
Median Absolute Deviation (MAD)71
Skewness-3.916413992
Sum10790794
Variance136293.2827
MonotonicityNot monotonic
2022-01-08T23:23:08.813267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200875
 
1.3%
200473
 
1.2%
200367
 
1.1%
201865
 
1.1%
200762
 
1.0%
201061
 
1.0%
201761
 
1.0%
200660
 
1.0%
201959
 
1.0%
200958
 
1.0%
Other values (912)5322
89.3%
ValueCountFrequency (%)
-21501
< 0.1%
-20002
< 0.1%
-16101
< 0.1%
-15661
< 0.1%
-14501
< 0.1%
-12501
< 0.1%
-10501
< 0.1%
-7591
< 0.1%
-5901
< 0.1%
-5251
< 0.1%
ValueCountFrequency (%)
202135
0.6%
202029
0.5%
201959
1.0%
201865
1.1%
201761
1.0%
201652
0.9%
201546
0.8%
201451
0.9%
201353
0.9%
201244
0.7%

Tsu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1808
Distinct (%)99.9%
Missing4154
Missing (%)69.7%
Infinite0
Infinite (%)0.0%
Mean2257.036484
Minimum1
Maximum5821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:09.401844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile192.2
Q11016
median1879
Q33134
95-th percentile5662.2
Maximum5821
Range5820
Interquartile range (IQR)2118

Descriptive statistics

Standard deviation1666.248914
Coefficient of variation (CV)0.7382463357
Kurtosis-0.2614910128
Mean2257.036484
Median Absolute Deviation (MAD)985
Skewness0.8733344802
Sum4082979
Variance2776385.443
MonotonicityNot monotonic
2022-01-08T23:23:09.779801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19262
 
< 0.1%
3121
 
< 0.1%
21791
 
< 0.1%
18081
 
< 0.1%
31451
 
< 0.1%
34101
 
< 0.1%
34191
 
< 0.1%
2781
 
< 0.1%
32081
 
< 0.1%
431
 
< 0.1%
Other values (1798)1798
30.2%
(Missing)4154
69.7%
ValueCountFrequency (%)
11
< 0.1%
31
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
121
< 0.1%
191
< 0.1%
231
< 0.1%
261
< 0.1%
ValueCountFrequency (%)
58211
< 0.1%
58201
< 0.1%
58191
< 0.1%
58181
< 0.1%
58171
< 0.1%
58161
< 0.1%
58151
< 0.1%
58111
< 0.1%
58091
< 0.1%
58061
< 0.1%

Vol
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct71
Distinct (%)100.0%
Missing5892
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean3714.774648
Minimum16
Maximum7542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:10.083990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile529
Q11344
median3833
Q35405.5
95-th percentile7538.5
Maximum7542
Range7526
Interquartile range (IQR)4061.5

Descriptive statistics

Standard deviation2447.753099
Coefficient of variation (CV)0.6589237117
Kurtosis-1.26010209
Mean3714.774648
Median Absolute Deviation (MAD)2482
Skewness0.0995489924
Sum263749
Variance5991495.234
MonotonicityNot monotonic
2022-01-08T23:23:10.287134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28821
 
< 0.1%
49071
 
< 0.1%
4891
 
< 0.1%
72441
 
< 0.1%
75391
 
< 0.1%
13461
 
< 0.1%
33141
 
< 0.1%
75371
 
< 0.1%
74441
 
< 0.1%
13501
 
< 0.1%
Other values (61)61
 
1.0%
(Missing)5892
98.8%
ValueCountFrequency (%)
161
< 0.1%
431
< 0.1%
3531
< 0.1%
4891
< 0.1%
5691
< 0.1%
5811
< 0.1%
5851
< 0.1%
6641
< 0.1%
6791
< 0.1%
7521
< 0.1%
ValueCountFrequency (%)
75421
< 0.1%
75411
< 0.1%
75401
< 0.1%
75391
< 0.1%
75381
< 0.1%
75371
< 0.1%
75361
< 0.1%
75041
< 0.1%
74441
< 0.1%
72841
< 0.1%

Location Name
Categorical

HIGH CARDINALITY

Distinct3753
Distinct (%)62.9%
Missing1
Missing (%)< 0.1%
Memory size46.7 KiB
CHINA: YUNNAN PROVINCE
 
67
RUSSIA: KURIL ISLANDS
 
53
TURKEY
 
44
CHINA: SICHUAN PROVINCE
 
42
VANUATU ISLANDS
 
32
Other values (3748)
5724 

Length

Max length52
Median length23
Mean length24.56239517
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3113 ?
Unique (%)52.2%

Sample

1st rowTURKEY: LICE
2nd rowTAIWAN: HUALIEN
3rd rowGREECE: S
4th rowTURKEY
5th rowMEXICO

Common Values

ValueCountFrequency (%)
CHINA: YUNNAN PROVINCE67
 
1.1%
RUSSIA: KURIL ISLANDS53
 
0.9%
TURKEY44
 
0.7%
CHINA: SICHUAN PROVINCE42
 
0.7%
VANUATU ISLANDS32
 
0.5%
BALKANS NW: CROATIA30
 
0.5%
SOLOMON ISLANDS29
 
0.5%
PERU26
 
0.4%
CHINA: GANSU PROVINCE25
 
0.4%
SWITZERLAND25
 
0.4%
Other values (3743)5589
93.7%

Length

2022-01-08T23:23:10.506768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china590
 
3.2%
province523
 
2.9%
islands412
 
2.3%
japan395
 
2.2%
iran375
 
2.1%
indonesia371
 
2.0%
turkey319
 
1.8%
italy306
 
1.7%
greece247
 
1.4%
new239
 
1.3%
Other values (4264)14383
79.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Latitude
Real number (ℝ)

HIGH CORRELATION

Distinct127
Distinct (%)2.1%
Missing49
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean22.36980047
Minimum-63
Maximum73
Zeros36
Zeros (%)0.6%
Negative1136
Negative (%)19.1%
Memory size46.7 KiB
2022-01-08T23:23:10.769198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-63
5-th percentile-23
Q110
median32
Q339
95-th percentile46
Maximum73
Range136
Interquartile range (IQR)29

Descriptive statistics

Standard deviation22.9016982
Coefficient of variation (CV)1.023777491
Kurtosis0.3878274207
Mean22.36980047
Median Absolute Deviation (MAD)10
Skewness-1.033648504
Sum132295
Variance524.4877805
MonotonicityNot monotonic
2022-01-08T23:23:11.066755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38362
 
6.1%
36300
 
5.0%
39256
 
4.3%
40232
 
3.9%
41226
 
3.8%
37223
 
3.7%
35205
 
3.4%
34151
 
2.5%
43135
 
2.3%
44134
 
2.2%
Other values (117)3690
61.9%
ValueCountFrequency (%)
-631
 
< 0.1%
-621
 
< 0.1%
-612
< 0.1%
-602
< 0.1%
-592
< 0.1%
-583
0.1%
-562
< 0.1%
-551
 
< 0.1%
-544
0.1%
-532
< 0.1%
ValueCountFrequency (%)
731
 
< 0.1%
663
 
0.1%
652
 
< 0.1%
6413
0.2%
621
 
< 0.1%
617
0.1%
606
0.1%
593
 
0.1%
588
0.1%
5711
0.2%

Longitude
Real number (ℝ)

HIGH CORRELATION

Distinct328
Distinct (%)5.5%
Missing49
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean38.1061887
Minimum-180
Maximum180
Zeros17
Zeros (%)0.3%
Negative1540
Negative (%)25.8%
Memory size46.7 KiB
2022-01-08T23:23:11.350862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-180
5-th percentile-104
Q1-9
median44
Q3115
95-th percentile152
Maximum180
Range360
Interquartile range (IQR)124

Descriptive statistics

Standard deviation86.69290993
Coefficient of variation (CV)2.275034919
Kurtosis-0.7735401149
Mean38.1061887
Median Absolute Deviation (MAD)69
Skewness-0.4656465378
Sum225360
Variance7515.660631
MonotonicityNot monotonic
2022-01-08T23:23:11.556313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12195
 
1.6%
-7193
 
1.6%
12281
 
1.4%
1581
 
1.4%
-7275
 
1.3%
2173
 
1.2%
3673
 
1.2%
-7072
 
1.2%
-7770
 
1.2%
1668
 
1.1%
Other values (318)5133
86.1%
ValueCountFrequency (%)
-1803
 
0.1%
-1794
 
0.1%
-17813
0.2%
-17710
0.2%
-17610
0.2%
-1758
0.1%
-1746
0.1%
-1735
 
0.1%
-1727
0.1%
-1711
 
< 0.1%
ValueCountFrequency (%)
1802
 
< 0.1%
17913
0.2%
17810
0.2%
1778
0.1%
1766
0.1%
1758
0.1%
1746
0.1%
17311
0.2%
17210
0.2%
1712
 
< 0.1%

Focal Depth (km)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct196
Distinct (%)6.2%
Missing2801
Missing (%)47.0%
Infinite0
Infinite (%)0.0%
Mean40.5170778
Minimum0
Maximum675
Zeros14
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:11.771572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median25
Q339
95-th percentile120
Maximum675
Range675
Interquartile range (IQR)29

Descriptive statistics

Standard deviation70.07431672
Coefficient of variation (CV)1.729500757
Kurtosis43.2963092
Mean40.5170778
Median Absolute Deviation (MAD)15
Skewness6.017458179
Sum128115
Variance4910.409863
MonotonicityNot monotonic
2022-01-08T23:23:12.124364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10474
 
7.9%
33368
 
6.2%
15126
 
2.1%
60120
 
2.0%
2097
 
1.6%
589
 
1.5%
2573
 
1.2%
3067
 
1.1%
1265
 
1.1%
4064
 
1.1%
Other values (186)1619
27.2%
(Missing)2801
47.0%
ValueCountFrequency (%)
014
 
0.2%
113
 
0.2%
29
 
0.2%
315
 
0.3%
420
 
0.3%
589
1.5%
636
0.6%
743
0.7%
853
0.9%
941
0.7%
ValueCountFrequency (%)
6751
< 0.1%
6701
< 0.1%
6641
< 0.1%
6511
< 0.1%
6502
< 0.1%
6401
< 0.1%
6361
< 0.1%
6331
< 0.1%
6311
< 0.1%
6291
< 0.1%

Mag
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)0.2%
Missing1679
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean6.54248366
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:12.296331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q16
median7
Q37
95-th percentile8
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.09987667
Coefficient of variation (CV)0.1681130175
Kurtosis-0.309326104
Mean6.54248366
Median Absolute Deviation (MAD)1
Skewness-0.2602109155
Sum28028
Variance1.209728689
MonotonicityNot monotonic
2022-01-08T23:23:12.429650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
71313
22.0%
61286
21.6%
8885
14.8%
5642
 
10.8%
4102
 
1.7%
942
 
0.7%
39
 
0.2%
24
 
0.1%
101
 
< 0.1%
(Missing)1679
28.2%
ValueCountFrequency (%)
24
 
0.1%
39
 
0.2%
4102
 
1.7%
5642
10.8%
61286
21.6%
71313
22.0%
8885
14.8%
942
 
0.7%
101
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
942
 
0.7%
8885
14.8%
71313
22.0%
61286
21.6%
5642
10.8%
4102
 
1.7%
39
 
0.2%
24
 
0.1%

MMI Int
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct11
Distinct (%)0.4%
Missing3157
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean8.248752673
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:12.580581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17
median8
Q310
95-th percentile11
Maximum12
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.806146234
Coefficient of variation (CV)0.2189599211
Kurtosis0.2007898038
Mean8.248752673
Median Absolute Deviation (MAD)1
Skewness-0.4446667522
Sum23146
Variance3.262164219
MonotonicityNot monotonic
2022-01-08T23:23:12.720339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8625
 
10.5%
10588
 
9.9%
9503
 
8.4%
7466
 
7.8%
6220
 
3.7%
11128
 
2.1%
5115
 
1.9%
1271
 
1.2%
461
 
1.0%
320
 
0.3%
(Missing)3157
52.9%
ValueCountFrequency (%)
29
 
0.2%
320
 
0.3%
461
 
1.0%
5115
 
1.9%
6220
 
3.7%
7466
7.8%
8625
10.5%
9503
8.4%
10588
9.9%
11128
 
2.1%
ValueCountFrequency (%)
1271
 
1.2%
11128
 
2.1%
10588
9.9%
9503
8.4%
8625
10.5%
7466
7.8%
6220
 
3.7%
5115
 
1.9%
461
 
1.0%
320
 
0.3%

Deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct404
Distinct (%)20.2%
Missing3962
Missing (%)66.4%
Infinite0
Infinite (%)0.0%
Mean3613.196402
Minimum1
Maximum830000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:12.968114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median21
Q3300
95-th percentile13000
Maximum830000
Range829999
Interquartile range (IQR)297

Descriptive statistics

Standard deviation25162.77812
Coefficient of variation (CV)6.964132398
Kurtosis612.3145464
Mean3613.196402
Median Absolute Deviation (MAD)20
Skewness21.11729164
Sum7230006
Variance633165402.8
MonotonicityNot monotonic
2022-01-08T23:23:13.224697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1283
 
4.7%
2161
 
2.7%
387
 
1.5%
467
 
1.1%
553
 
0.9%
641
 
0.7%
738
 
0.6%
831
 
0.5%
2029
 
0.5%
200029
 
0.5%
Other values (394)1182
 
19.8%
(Missing)3962
66.4%
ValueCountFrequency (%)
1283
4.7%
2161
2.7%
387
 
1.5%
467
 
1.1%
553
 
0.9%
641
 
0.7%
738
 
0.6%
831
 
0.5%
926
 
0.4%
1028
 
0.5%
ValueCountFrequency (%)
8300001
< 0.1%
3160001
< 0.1%
2600001
< 0.1%
2500001
< 0.1%
2427691
< 0.1%
2000002
< 0.1%
1500001
< 0.1%
1428071
< 0.1%
1300001
< 0.1%
1100001
< 0.1%

Death Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.2%
Missing3511
Missing (%)58.9%
Memory size46.7 KiB
1.0
1269 
3.0
583 
4.0
351 
2.0
248 
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4.0
2nd row1.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01269
 
21.3%
3.0583
 
9.8%
4.0351
 
5.9%
2.0248
 
4.2%
0.01
 
< 0.1%
(Missing)3511
58.9%

Length

2022-01-08T23:23:13.579750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:13.870971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.01269
51.8%
3.0583
23.8%
4.0351
 
14.3%
2.0248
 
10.1%
0.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Missing
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)83.3%
Missing5939
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean1941.958333
Minimum1
Maximum43476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:14.178147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median19
Q3135.5
95-th percentile785
Maximum43476
Range43475
Interquartile range (IQR)131.75

Descriptive statistics

Standard deviation8849.970005
Coefficient of variation (CV)4.557239902
Kurtosis23.95984787
Mean1941.958333
Median Absolute Deviation (MAD)18
Skewness4.893123659
Sum46607
Variance78321969.09
MonotonicityNot monotonic
2022-01-08T23:23:14.658863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
13
 
0.1%
33
 
0.1%
1141
 
< 0.1%
151
 
< 0.1%
681
 
< 0.1%
171
 
< 0.1%
6671
 
< 0.1%
91
 
< 0.1%
701
 
< 0.1%
3291
 
< 0.1%
Other values (10)10
 
0.2%
(Missing)5939
99.6%
ValueCountFrequency (%)
13
0.1%
33
0.1%
41
 
< 0.1%
51
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
151
 
< 0.1%
171
 
< 0.1%
211
 
< 0.1%
301
 
< 0.1%
ValueCountFrequency (%)
434761
< 0.1%
8001
< 0.1%
7001
< 0.1%
6671
< 0.1%
3291
< 0.1%
2001
< 0.1%
1141
< 0.1%
701
< 0.1%
681
< 0.1%
621
< 0.1%

Missing Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)16.7%
Missing5939
Missing (%)99.6%
Memory size46.7 KiB
1.0
13 
3.0
2.0
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.013
 
0.2%
3.06
 
0.1%
2.04
 
0.1%
4.01
 
< 0.1%
(Missing)5939
99.6%

Length

2022-01-08T23:23:15.169630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:15.457854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.013
54.2%
3.06
25.0%
2.04
 
16.7%
4.01
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Injuries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct346
Distinct (%)27.8%
Missing4719
Missing (%)79.1%
Infinite0
Infinite (%)0.0%
Mean2266.669614
Minimum1
Maximum799000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:15.781990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median40
Q3200
95-th percentile3299.25
Maximum799000
Range798999
Interquartile range (IQR)190

Descriptive statistics

Standard deviation26435.78842
Coefficient of variation (CV)11.66283267
Kurtosis697.9841874
Mean2266.669614
Median Absolute Deviation (MAD)37
Skewness24.75840512
Sum2819737
Variance698850909.6
MonotonicityNot monotonic
2022-01-08T23:23:16.311574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156
 
0.9%
246
 
0.8%
20043
 
0.7%
10039
 
0.7%
339
 
0.7%
437
 
0.6%
535
 
0.6%
633
 
0.6%
3030
 
0.5%
2026
 
0.4%
Other values (336)860
 
14.4%
(Missing)4719
79.1%
ValueCountFrequency (%)
156
0.9%
246
0.8%
339
0.7%
437
0.6%
535
0.6%
633
0.6%
719
 
0.3%
818
 
0.3%
916
 
0.3%
1025
0.4%
ValueCountFrequency (%)
7990001
< 0.1%
3741711
< 0.1%
1668361
< 0.1%
1465991
< 0.1%
1050001
< 0.1%
1000001
< 0.1%
760001
< 0.1%
500002
< 0.1%
470001
< 0.1%
385681
< 0.1%

Injuries Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing4518
Missing (%)75.8%
Memory size46.7 KiB
1.0
730 
3.0
376 
2.0
193 
4.0
146 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0730
 
12.2%
3.0376
 
6.3%
2.0193
 
3.2%
4.0146
 
2.4%
(Missing)4518
75.8%

Length

2022-01-08T23:23:16.887034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:17.086499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0730
50.5%
3.0376
26.0%
2.0193
 
13.4%
4.0146
 
10.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Damage ($Mil)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct172
Distinct (%)34.0%
Missing5457
Missing (%)91.5%
Infinite0
Infinite (%)0.0%
Mean1210.284585
Minimum0
Maximum100000
Zeros21
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:17.283972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median24
Q3209
95-th percentile5000
Maximum100000
Range100000
Interquartile range (IQR)205

Descriptive statistics

Standard deviation6706.617517
Coefficient of variation (CV)5.54135581
Kurtosis146.3901291
Mean1210.284585
Median Absolute Deviation (MAD)23
Skewness11.22555577
Sum612404
Variance44978718.52
MonotonicityNot monotonic
2022-01-08T23:23:17.543225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157
 
1.0%
549
 
0.8%
226
 
0.4%
021
 
0.4%
314
 
0.2%
2013
 
0.2%
413
 
0.2%
2511
 
0.2%
811
 
0.2%
10010
 
0.2%
Other values (162)281
 
4.7%
(Missing)5457
91.5%
ValueCountFrequency (%)
021
 
0.4%
157
1.0%
226
0.4%
314
 
0.2%
413
 
0.2%
549
0.8%
66
 
0.1%
74
 
0.1%
811
 
0.2%
93
 
0.1%
ValueCountFrequency (%)
1000001
< 0.1%
860001
< 0.1%
400001
< 0.1%
300001
< 0.1%
280001
< 0.1%
200002
< 0.1%
162001
< 0.1%
158001
< 0.1%
150001
< 0.1%
140001
< 0.1%

Damage Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing1654
Missing (%)27.7%
Memory size46.7 KiB
2.0
1474 
3.0
1290 
1.0
1070 
4.0
475 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.01474
24.7%
3.01290
21.6%
1.01070
17.9%
4.0475
 
8.0%
(Missing)1654
27.7%

Length

2022-01-08T23:23:17.872880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:17.977599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.01474
34.2%
3.01290
29.9%
1.01070
24.8%
4.0475
 
11.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Houses Destroyed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct425
Distinct (%)54.0%
Missing5176
Missing (%)86.8%
Infinite0
Infinite (%)0.0%
Mean18227.3507
Minimum1
Maximum5360000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:18.137410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q170
median540
Q34134
95-th percentile53570.5
Maximum5360000
Range5359999
Interquartile range (IQR)4064

Descriptive statistics

Standard deviation197572.1422
Coefficient of variation (CV)10.83932303
Kurtosis682.8264957
Mean18227.3507
Median Absolute Deviation (MAD)529
Skewness25.40501293
Sum14344925
Variance3.903475139 × 1010
MonotonicityNot monotonic
2022-01-08T23:23:18.588174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119
 
0.3%
10018
 
0.3%
30015
 
0.3%
50015
 
0.3%
20014
 
0.2%
200012
 
0.2%
5012
 
0.2%
150011
 
0.2%
100010
 
0.2%
409
 
0.2%
Other values (415)652
 
10.9%
(Missing)5176
86.8%
ValueCountFrequency (%)
119
0.3%
29
0.2%
37
 
0.1%
44
 
0.1%
58
0.1%
65
 
0.1%
75
 
0.1%
87
 
0.1%
92
 
< 0.1%
107
 
0.1%
ValueCountFrequency (%)
53600001
< 0.1%
9590001
< 0.1%
4988521
< 0.1%
4120001
< 0.1%
4000001
< 0.1%
3920001
< 0.1%
3390001
< 0.1%
2648781
< 0.1%
2000002
< 0.1%
1500001
< 0.1%

Houses Destroyed Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.3%
Missing4259
Missing (%)71.4%
Memory size46.7 KiB
3.0
664 
4.0
425 
1.0
348 
2.0
266 
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row2.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0664
 
11.1%
4.0425
 
7.1%
1.0348
 
5.8%
2.0266
 
4.5%
0.01
 
< 0.1%
(Missing)4259
71.4%

Length

2022-01-08T23:23:18.832529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:18.948146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0664
39.0%
4.0425
24.9%
1.0348
20.4%
2.0266
15.6%
0.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Houses Damaged
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct321
Distinct (%)65.0%
Missing5469
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean25488.4251
Minimum1
Maximum5360000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:19.090994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.3
Q1100
median710
Q34000
95-th percentile75634.85
Maximum5360000
Range5359999
Interquartile range (IQR)3900

Descriptive statistics

Standard deviation248692.1254
Coefficient of variation (CV)9.757061271
Kurtosis432.1266608
Mean25488.4251
Median Absolute Deviation (MAD)682
Skewness20.24621769
Sum12591282
Variance6.184777324 × 1010
MonotonicityNot monotonic
2022-01-08T23:23:19.314139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10015
 
0.3%
50010
 
0.2%
509
 
0.2%
10008
 
0.1%
208
 
0.1%
20007
 
0.1%
2007
 
0.1%
7006
 
0.1%
66
 
0.1%
6006
 
0.1%
Other values (311)412
 
6.9%
(Missing)5469
91.7%
ValueCountFrequency (%)
14
0.1%
24
0.1%
33
0.1%
43
0.1%
52
 
< 0.1%
66
0.1%
72
 
< 0.1%
81
 
< 0.1%
103
0.1%
112
 
< 0.1%
ValueCountFrequency (%)
53600001
< 0.1%
9590001
< 0.1%
5000001
< 0.1%
4000001
< 0.1%
3920001
< 0.1%
3390001
< 0.1%
2648781
< 0.1%
2566971
< 0.1%
1883831
< 0.1%
1840001
< 0.1%

Houses Damaged Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.4%
Missing4992
Missing (%)83.7%
Memory size46.7 KiB
3.0
291 
4.0
250 
1.0
236 
2.0
194 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0291
 
4.9%
4.0250
 
4.2%
1.0236
 
4.0%
2.0194
 
3.3%
(Missing)4992
83.7%

Length

2022-01-08T23:23:19.500312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:19.606030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0291
30.0%
4.0250
25.7%
1.0236
24.3%
2.0194
20.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total Deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct401
Distinct (%)22.5%
Missing4184
Missing (%)70.2%
Infinite0
Infinite (%)0.0%
Mean3844.855537
Minimum1
Maximum830000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:19.781564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median18
Q3253.5
95-th percentile12000
Maximum830000
Range829999
Interquartile range (IQR)250.5

Descriptive statistics

Standard deviation27097.33474
Coefficient of variation (CV)7.047686053
Kurtosis513.8618059
Mean3844.855537
Median Absolute Deviation (MAD)17
Skewness19.31677834
Sum6839998
Variance734265550.2
MonotonicityNot monotonic
2022-01-08T23:23:20.064803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1278
 
4.7%
2159
 
2.7%
383
 
1.4%
456
 
0.9%
555
 
0.9%
635
 
0.6%
730
 
0.5%
828
 
0.5%
1127
 
0.5%
1225
 
0.4%
Other values (391)1003
 
16.8%
(Missing)4184
70.2%
ValueCountFrequency (%)
1278
4.7%
2159
2.7%
383
 
1.4%
456
 
0.9%
555
 
0.9%
635
 
0.6%
730
 
0.5%
828
 
0.5%
922
 
0.4%
1023
 
0.4%
ValueCountFrequency (%)
8300001
< 0.1%
3160001
< 0.1%
2600001
< 0.1%
2500001
< 0.1%
2427691
< 0.1%
2278991
< 0.1%
2000002
< 0.1%
1500001
< 0.1%
1428071
< 0.1%
1300001
< 0.1%

Total Death Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.2%
Missing3856
Missing (%)64.7%
Memory size46.7 KiB
1.0
1162 
3.0
450 
4.0
317 
2.0
178 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01162
 
19.5%
3.0450
 
7.5%
4.0317
 
5.3%
2.0178
 
3.0%
(Missing)3856
64.7%

Length

2022-01-08T23:23:20.288709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:20.501106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.01162
55.1%
3.0450
 
21.4%
4.0317
 
15.0%
2.0178
 
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total Missing
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)77.8%
Missing5936
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean1798.481481
Minimum1
Maximum43476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:20.661710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median30
Q3169
95-th percentile1468.5
Maximum43476
Range43475
Interquartile range (IQR)165

Descriptive statistics

Standard deviation8338.250621
Coefficient of variation (CV)4.636272715
Kurtosis26.87068241
Mean1798.481481
Median Absolute Deviation (MAD)29
Skewness5.178470429
Sum48559
Variance69526423.41
MonotonicityNot monotonic
2022-01-08T23:23:20.832221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
14
 
0.1%
33
 
0.1%
302
 
< 0.1%
3291
 
< 0.1%
151
 
< 0.1%
1381
 
< 0.1%
17551
 
< 0.1%
171
 
< 0.1%
6671
 
< 0.1%
91
 
< 0.1%
Other values (11)11
 
0.2%
(Missing)5936
99.5%
ValueCountFrequency (%)
14
0.1%
33
0.1%
51
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
151
 
< 0.1%
171
 
< 0.1%
211
 
< 0.1%
302
< 0.1%
621
 
< 0.1%
ValueCountFrequency (%)
434761
< 0.1%
17551
< 0.1%
8001
< 0.1%
7001
< 0.1%
6671
< 0.1%
3291
< 0.1%
2001
< 0.1%
1381
< 0.1%
1141
< 0.1%
1001
< 0.1%

Total Missing Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)13.3%
Missing5933
Missing (%)99.5%
Memory size46.7 KiB
1.0
17 
3.0
2.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.017
 
0.3%
3.08
 
0.1%
2.03
 
0.1%
4.02
 
< 0.1%
(Missing)5933
99.5%

Length

2022-01-08T23:23:21.009268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:21.131939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.017
56.7%
3.08
26.7%
2.03
 
10.0%
4.02
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total Injuries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct353
Distinct (%)27.9%
Missing4700
Missing (%)78.8%
Infinite0
Infinite (%)0.0%
Mean2463.782264
Minimum1
Maximum799000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:21.283534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median40
Q3200
95-th percentile3283.8
Maximum799000
Range798999
Interquartile range (IQR)190

Descriptive statistics

Standard deviation27536.04023
Coefficient of variation (CV)11.17632862
Kurtosis593.7097416
Mean2463.782264
Median Absolute Deviation (MAD)37
Skewness22.55838787
Sum3111757
Variance758233511.5
MonotonicityNot monotonic
2022-01-08T23:23:21.645487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160
 
1.0%
246
 
0.8%
20042
 
0.7%
10041
 
0.7%
340
 
0.7%
439
 
0.7%
534
 
0.6%
633
 
0.6%
3027
 
0.5%
2026
 
0.4%
Other values (343)875
 
14.7%
(Missing)4700
78.8%
ValueCountFrequency (%)
160
1.0%
246
0.8%
340
0.7%
439
0.7%
534
0.6%
633
0.6%
722
 
0.4%
816
 
0.3%
916
 
0.3%
1025
0.4%
ValueCountFrequency (%)
7990001
< 0.1%
3741711
< 0.1%
3000001
< 0.1%
1668361
< 0.1%
1465991
< 0.1%
1050001
< 0.1%
1000001
< 0.1%
760001
< 0.1%
500002
< 0.1%
470001
< 0.1%

Total Injuries Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing4498
Missing (%)75.4%
Memory size46.7 KiB
1.0
739 
3.0
377 
2.0
202 
4.0
147 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0739
 
12.4%
3.0377
 
6.3%
2.0202
 
3.4%
4.0147
 
2.5%
(Missing)4498
75.4%

Length

2022-01-08T23:23:21.845835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:21.946403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0739
50.4%
3.0377
25.7%
2.0202
 
13.8%
4.0147
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total Damage ($Mil)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct174
Distinct (%)36.4%
Missing5485
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean1753.046025
Minimum0
Maximum220137
Zeros16
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:22.086416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median27
Q3264.25
95-th percentile5345
Maximum220137
Range220137
Interquartile range (IQR)259.25

Descriptive statistics

Standard deviation12154.37955
Coefficient of variation (CV)6.933291756
Kurtosis232.555246
Mean1753.046025
Median Absolute Deviation (MAD)26
Skewness14.16524399
Sum837956
Variance147728942.3
MonotonicityNot monotonic
2022-01-08T23:23:22.273608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154
 
0.9%
542
 
0.7%
224
 
0.4%
016
 
0.3%
413
 
0.2%
311
 
0.2%
2511
 
0.2%
810
 
0.2%
2010
 
0.2%
1009
 
0.2%
Other values (164)278
 
4.7%
(Missing)5485
92.0%
ValueCountFrequency (%)
016
 
0.3%
154
0.9%
224
0.4%
311
 
0.2%
413
 
0.2%
542
0.7%
67
 
0.1%
73
 
0.1%
810
 
0.2%
93
 
0.1%
ValueCountFrequency (%)
2201371
< 0.1%
1000001
< 0.1%
860001
< 0.1%
400001
< 0.1%
300001
< 0.1%
280001
< 0.1%
200002
< 0.1%
162001
< 0.1%
158001
< 0.1%
150001
< 0.1%

Total Damage Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing2632
Missing (%)44.1%
Memory size46.7 KiB
2.0
1049 
1.0
1010 
3.0
809 
4.0
463 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.01049
 
17.6%
1.01010
 
16.9%
3.0809
 
13.6%
4.0463
 
7.8%
(Missing)2632
44.1%

Length

2022-01-08T23:23:22.438570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:22.554616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.01049
31.5%
1.01010
30.3%
3.0809
24.3%
4.0463
13.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total Houses Destroyed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct432
Distinct (%)52.9%
Missing5146
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean18798.04774
Minimum1
Maximum5360000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:22.728320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q167
median540
Q34000
95-th percentile53829.2
Maximum5360000
Range5359999
Interquartile range (IQR)3933

Descriptive statistics

Standard deviation195579.1795
Coefficient of variation (CV)10.40422826
Kurtosis684.6438969
Mean18798.04774
Median Absolute Deviation (MAD)530
Skewness25.26872884
Sum15358005
Variance3.825121544 × 1010
MonotonicityNot monotonic
2022-01-08T23:23:22.957709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123
 
0.4%
10019
 
0.3%
50015
 
0.3%
30015
 
0.3%
20013
 
0.2%
150013
 
0.2%
200012
 
0.2%
5012
 
0.2%
100010
 
0.2%
210
 
0.2%
Other values (422)675
 
11.3%
(Missing)5146
86.3%
ValueCountFrequency (%)
123
0.4%
210
0.2%
37
 
0.1%
44
 
0.1%
58
 
0.1%
65
 
0.1%
77
 
0.1%
86
 
0.1%
93
 
0.1%
107
 
0.1%
ValueCountFrequency (%)
53600001
< 0.1%
9590001
< 0.1%
6950001
< 0.1%
4988521
< 0.1%
4120001
< 0.1%
4000001
< 0.1%
3920001
< 0.1%
3390001
< 0.1%
2648781
< 0.1%
2500001
< 0.1%

Total Houses Destroyed Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.2%
Missing4183
Missing (%)70.1%
Memory size46.7 KiB
3.0
692 
4.0
447 
1.0
358 
2.0
283 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0692
 
11.6%
4.0447
 
7.5%
1.0358
 
6.0%
2.0283
 
4.7%
(Missing)4183
70.1%

Length

2022-01-08T23:23:23.180929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:23.292629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0692
38.9%
4.0447
25.1%
1.0358
20.1%
2.0283
15.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total Houses Damaged
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct299
Distinct (%)67.8%
Missing5522
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean57838.43084
Minimum1
Maximum21000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2022-01-08T23:23:23.463173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q1100
median700
Q33288
95-th percentile54000
Maximum21000000
Range20999999
Interquartile range (IQR)3188

Descriptive statistics

Standard deviation1000249.644
Coefficient of variation (CV)17.29385859
Kurtosis439.6922174
Mean57838.43084
Median Absolute Deviation (MAD)669
Skewness20.95380115
Sum25506748
Variance1.000499351 × 1012
MonotonicityNot monotonic
2022-01-08T23:23:23.657939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10015
 
0.3%
209
 
0.2%
7008
 
0.1%
10008
 
0.1%
5007
 
0.1%
507
 
0.1%
50005
 
0.1%
6005
 
0.1%
905
 
0.1%
65
 
0.1%
Other values (289)367
 
6.2%
(Missing)5522
92.6%
ValueCountFrequency (%)
11
 
< 0.1%
23
0.1%
31
 
< 0.1%
43
0.1%
51
 
< 0.1%
65
0.1%
71
 
< 0.1%
82
 
< 0.1%
103
0.1%
121
 
< 0.1%
ValueCountFrequency (%)
210000001
< 0.1%
5000001
< 0.1%
2809201
< 0.1%
2566971
< 0.1%
1883831
< 0.1%
1840001
< 0.1%
1816651
< 0.1%
1696321
< 0.1%
1630001
< 0.1%
1604001
< 0.1%

Total Houses Damaged Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.5%
Missing5099
Missing (%)85.5%
Memory size46.7 KiB
3.0
244 
1.0
223 
4.0
218 
2.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0244
 
4.1%
1.0223
 
3.7%
4.0218
 
3.7%
2.0179
 
3.0%
(Missing)5099
85.5%

Length

2022-01-08T23:23:23.875954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T23:23:23.980042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0244
28.2%
1.0223
25.8%
4.0218
25.2%
2.0179
20.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

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2022-01-08T23:22:04.922865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:11.974977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:18.188355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:24.797679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:30.410370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:35.946117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:39.505663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:46.122327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:50.320294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:53.754771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:57.772795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:21:28.600298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:21:32.284678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:21:36.197027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:21:40.402553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:21:44.001022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:21:47.666135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:21:52.028219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:21:55.754789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:21:59.426187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:05.238989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:12.301102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:18.442677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:25.161706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:30.567346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:36.172543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:39.747018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:46.328775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:50.458184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T23:22:53.942466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-01-08T23:23:24.204440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-08T23:23:24.927609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-08T23:23:25.480219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-08T23:23:25.999338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-08T23:23:26.377677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-08T23:22:58.646460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-08T23:23:02.004480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-08T23:23:03.863505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-08T23:23:07.103841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

YearTsuVolLocation NameLatitudeLongitudeFocal Depth (km)MagMMI IntDeathsDeath DescriptionMissingMissing DescriptionInjuriesInjuries DescriptionDamage ($Mil)Damage DescriptionHouses DestroyedHouses Destroyed DescriptionHouses DamagedHouses Damaged DescriptionTotal DeathsTotal Death DescriptionTotal MissingTotal Missing DescriptionTotal InjuriesTotal Injuries DescriptionTotal Damage ($Mil)Total Damage DescriptionTotal Houses DestroyedTotal Houses Destroyed DescriptionTotal Houses DamagedTotal Houses Damaged Description
01975NaNNaNTURKEY: LICE38.041.026.07.09.02311.04.0NaNNaN3372.04.017.03.0NaNNaNNaNNaN2311.04.0NaNNaN3372.04.0NaNNaNNaNNaNNaNNaN
12018NaNNaNTAIWAN: HUALIEN24.0122.017.06.0NaN17.01.0NaNNaN291.03.0NaN2.04.01.0NaN1.017.01.0NaNNaN291.03.0NaN2.04.01.0NaN1.0
21622236.0NaNGREECE: S38.021.0NaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31876NaNNaNTURKEY39.031.0NaNNaN10.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41611NaNNaNMEXICO20.0-99.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
52006NaNNaNINDONESIA: SUMATRA: KALIANDAK-6.0105.017.06.0NaNNaNNaNNaNNaNNaNNaNNaN1.0NaN2.0NaN2.0NaNNaNNaNNaNNaNNaNNaN1.0NaN2.0NaNNaN
62003NaNNaNINDONESIA: FLORES, RUTENG-8.0121.033.05.0NaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaN1.0
71861909.0NaNINDONESIA: SW. SUMATRA1.098.070.07.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
81858NaNNaNPORTUGAL: SETUBAL38.0-9.0NaNNaN10.06.01.0NaNNaNNaNNaNNaN3.0605.03.0605.03.06.01.0NaNNaNNaNNaNNaN3.0605.03.0NaNNaN
92009NaNNaNSAUDI ARABIA: WESTERN25.038.02.06.0NaNNaNNaNNaNNaN7.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN7.01.0NaNNaNNaNNaNNaNNaN

Last rows

YearTsuVolLocation NameLatitudeLongitudeFocal Depth (km)MagMMI IntDeathsDeath DescriptionMissingMissing DescriptionInjuriesInjuries DescriptionDamage ($Mil)Damage DescriptionHouses DestroyedHouses Destroyed DescriptionHouses DamagedHouses Damaged DescriptionTotal DeathsTotal Death DescriptionTotal MissingTotal Missing DescriptionTotal InjuriesTotal Injuries DescriptionTotal Damage ($Mil)Total Damage DescriptionTotal Houses DestroyedTotal Houses Destroyed DescriptionTotal Houses DamagedTotal Houses Damaged Description
59532017NaNNaNITALY: ISCHIA ISLAND41.014.03.04.0NaN2.01.0NaNNaN42.01.0NaN2.0NaN2.0NaN2.02.01.0NaNNaN42.01.0NaN2.0NaN2.0NaN2.0
59541599NaNNaNPHILIPPINES: MANILA15.0121.0NaNNaN8.0NaNNaNNaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
595519932222.0NaNRUSSIA: KAMCHATKA52.0159.034.07.0NaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaN
59561883NaNNaNCOLOMBIA: ANTIOQUIA, YARUMAL7.0-77.0NaN6.07.0NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaN
59571761NaNNaNCHINA: YUNNAN PROVINCE24.0103.0NaN6.08.0120.03.0NaNNaN120.03.0NaN3.01900.04.0NaNNaN120.03.0NaNNaN120.03.0NaN3.01900.04.0NaNNaN
59581846NaNNaNCHILE: COIAPO-27.0-70.0NaNNaN8.0NaNNaNNaNNaNNaNNaNNaN2.0NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaN2.0NaN3.0NaNNaN
59591721NaNNaNIRAN: TABRIZ38.047.0NaN8.0NaN40000.04.0NaNNaNNaNNaNNaN4.0NaN4.0NaNNaN40000.04.0NaNNaNNaNNaNNaN4.0NaN4.0NaNNaN
59601573NaNNaNCHINA: GANSU PROVINCE: MINXIAN34.0104.0NaN7.09.0NaN3.0NaNNaNNaNNaNNaN3.0NaN3.0NaNNaNNaN3.0NaNNaNNaNNaNNaN3.0NaN3.0NaNNaN
59611001NaNNaNITALY: VERONA45.011.0NaN6.0NaNNaN3.0NaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
596219481783.0NaNINDONESIA: OFF NORTHWEST COAST6.095.0NaN6.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN